Educational wine recommendations from initially sparse data

根据最初稀疏的数据进行教育性葡萄酒推荐

• 批准号: 522760-2017
• 负责人: Cooperstock, Jeremy
• 金额: $ 1.82万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639603
• 关键词: Educational; wine; recommendations; initially; sparse

Wineout aims to democratize knowledge and appreciation of wine through the use of technology. Their initialeffort in this area was a game-based app intended for non-expert wine drinkers, but this requires an initialunderstanding of wine characteristics that is beyond the knowledge of most consumers. To further advanceWineout's mission, the present project was formulated between the company and university to design andprototype development of a recommendation system for wines that begins with limited user data, and overtime, becomes tailored to the individual consumer's tastes and profiles of similar users. The objective is notonly to offer recommendations that the user is likely to enjoy, but also to help educate users as to specificcharacteristics of the wines. Much of this project can be viewed as a conventional machine learning challenge,but there is an arguably even more important component that relates to the user experience. Thus, significanteffort will be allocated to gaining an understanding of how the target audience for the app currently makes theirwine selections, and ensuring that the app supports existing habits.

Wineout 旨在通过技术的使用使葡萄酒知识和欣赏民主化。他们在这一领域的最初努力是为非专业葡萄酒饮用者开发基于游戏的应用程序，但这需要对葡萄酒特性的初步了解，而这超出了大多数消费者的知识范围。为了进一步推进 Wineout 的使命，公司和大学共同制定了当前的项目，以设计和原型开发葡萄酒推荐系统，该系统从有限的用户数据开始，随着时间的推移，逐渐适应个人消费者的口味和类似用户的概况。目的不仅是提供用户可能喜欢的推荐，而且还帮助用户了解葡萄酒的具体特征。该项目的大部分内容可以被视为传统的机器学习挑战，但可以说还有一个与用户体验相关的更重要的组成部分。因此，我们将投入大量精力来了解应用程序的目标受众目前如何选择葡萄酒，并确保应用程序支持现有的习惯。